def yahoo_finance_functions():
si.get_data("AAPL")
si.get_day_gainers()
si.get_day_losers()
si.get_day_most_active()
si.get_analysts_info("AAPL")
stock = si.get_live_price("AAPL")
def import_yahoofinance2():
# import stock_info module from yahoo_fin
from yahoo_fin import stock_info as si
# get live price of Apple
si.get_live_price("aapl")
# or Amazon
si.get_live_price("amzn")
# or any other ticker
si.get_live_price(ticker)
# get quote table back as a data frame
si.get_quote_table("aapl", dict_result = False)
# or get it back as a dictionary (default)
si.get_quote_table("aapl")
si.get_top_crypto()
# get most active stocks on the day
si.get_day_most_active()
# get biggest gainers
si.get_day_gainers()
# get worst performers
si.get_day_losers()
def getDayMovers():
global gainers, losers, activity, gainerdata, loserdata, activedata
gainerdata = pd.DataFrame(si.get_day_gainers())[['Symbol', 'Price (Intraday)', '% Change']]
gainerdata['Price (Intraday)'] = "$" + gainerdata['Price (Intraday)'].astype(str)
gainerdata['% Change'] = "+" + gainerdata['% Change'].astype(str) + "%"
gainerdata['Combined'] = gainerdata[gainerdata.columns[0:]].apply(
lambda x: ':'.join(x.dropna().astype(str)),
axis=1
)
loserdata = pd.DataFrame(si.get_day_losers())[['Symbol', 'Price (Intraday)', '% Change']]
loserdata['Price (Intraday)'] = "$" + loserdata['Price (Intraday)'].astype(str)
loserdata['% Change'] = loserdata['% Change'].astype(str) + "%"
loserdata['Combined'] = loserdata[loserdata.columns[0:]].apply(
lambda x: ':'.join(x.dropna().astype(str)),
axis=1
)
activedata = pd.DataFrame(si.get_day_most_active())[['Symbol', 'Price (Intraday)', '% Change']]
activedata['Price (Intraday)'] = "$" + activedata['Price (Intraday)'].astype(str)
activedata['% Change'] = activedata['% Change'].astype(str) + "%"
activedata['Combined'] = activedata[activedata.columns[0:]].apply(
lambda x: ':'.join(x.dropna().astype(str)),
axis=1
)
# sets the list box data
gainers.set('\n'.join(gainerdata['Combined']))
losers.set('\n'.join(loserdata['Combined']))
activity.set('\n'.join(activedata['Combined']))
def post_market(self):
root = {}
root["market"] = {}
""" Market Node """
day_gainers = si.get_day_gainers().to_dict(orient="dict")
day_losers = si.get_day_losers().to_dict(orient="dict")
top_crypto = si.get_top_crypto().to_dict(orient="dict")
most_active = si.get_day_most_active().to_dict(orient="dict")
new_node = {"Day_Gainers" : day_gainers,
"Day_Losers" : day_losers,
"Top_Crypto" : top_crypto,
"Most_Active" : most_active}
for node_key in new_node.keys():
now = str(datetime.date(datetime.now()))
key_var = now
self.post(key_var, new_node[node_key], "Market", node_key)
time.sleep(.5)
print('Sucess\n')
def get_stocks():
arr = []
gainers = si.get_day_gainers().head(5)
tickers = gainers.Symbol
for i in tickers:
arr.append(i)
return arr
def submit():
if request.method == 'POST':
if request.form.get("TopGainers"):
top_gainers = si.get_day_gainers()
file_name = "TopGainers-"+ str(datetime.today()).split()[0] + '.csv'
return Response(
top_gainers.to_csv(),
mimetype="text/csv",
headers={"Content-disposition":
f"attachment; filename={file_name}"})
elif request.form.get("TopLosers"):
top_losers = si.get_day_losers()
file_name = "TopLosers-"+ str(datetime.today()).split()[0] + '.csv'
return Response(
top_losers.to_csv(),
mimetype="text/csv",
headers={"Content-disposition":
f"attachment; filename={file_name}"})
elif request.form.get("MostActives"):
most_active_stock = si.get_day_most_active()
file_name = "MostActives-" + str(datetime.today()).split()[0] + '.csv'
return Response(
most_active_stock.to_csv(),
mimetype="text/csv",
headers={"Content-disposition":
f"attachment; filename={file_name}.csv"})
def gainer():
gainers=get_day_gainers()
day_gainer1=gainers['Symbol']
price_day_gainer1=gainers['Price (Intraday)']
change_day_gainer1=gainers['% Change']
dg1_tick=(day_gainer1[0])
dg1_price=('${:,.2f}'.format((price_day_gainer1[0])))
dg1_change=('{:.2%}'.format(change_day_gainer1[0]))
day_gainer2=gainers['Symbol']
price_day_gainer2=gainers['Price (Intraday)']
change_day_gainer2=gainers['% Change']
dg2_tick=(day_gainer2[1])
dg2_price=('${:,.2f}'.format(price_day_gainer2[1]))
dg2_change=('{:.2%}'.format(change_day_gainer2[1]))
day_gainer3=gainers['Symbol']
price_day_gainer3=gainers['Price (Intraday)']
change_day_gainer3=gainers['% Change']
dg3_tick=(day_gainer3[2])
dg3_price=('${:,.2f}'.format(price_day_gainer3[2]))
dg3_change=('{:.2%}'.format(change_day_gainer3[2]))
tickers=(f"The top gainers are {dg1_tick}, {dg2_tick}, and {dg3_tick}.\n")
prices=(f"Their prices are currently {dg1_price}, {dg2_price}, and {dg3_price}.\n")
change=(f"Their prices rose by {dg1_change}, {dg2_change}, and {dg3_change}.")
print(tickers+prices+change)
def Daily_info():
win = yf.get_day_gainers()
win = win.sort_values(by='% Change',ascending = False).head(5)
lose = yf.get_day_losers()
lose = lose.sort_values(by='% Change',ascending = True).head(5)
active = yf.get_day_most_active()
active = active.head(5)
return win,lose,active
async def ganadores(ctx):
await ctx.send("espera.. OwO")
temp = si.get_day_gainers().head(10).drop(
["Market Cap", "Volume", "Avg Vol (3 month)"], axis=1)
await ctx.send(embed=crearEmbed(
ctx, temp, 'Ganadores', 'Lista de top 10 Ganadores',
'https://wompampsupport.azureedge.net/fetchimage?siteId=7575&v=2&jpgQuality=100&width=700&url=https%3A%2F%2Fi.kym-cdn.com%2Fentries%2Ficons%2Ffacebook%2F000%2F029%2F959%2FScreen_Shot_2019-06-05_at_1.26.32_PM.jpg',
0x00FF00))
def biggestgainer():
"""Returns: stock with the biggest gains in a given day and
its stock price with format: 'TICKER': 'PRICE'."""
day_gainer = si.get_day_gainers()
output = str(day_gainer.at[0, 'Symbol']) + ': ' + str(
round(si.get_live_price(day_gainer.at[0, 'Symbol']), roundNumber))
return output
def get_top_tickers():
top_tickers = si.get_day_gainers()
top_array = []
for x in range(10):
top_array.append((str(top_tickers['Symbol'][x])))
return jsonify(top_array)
def get_top_gainers():
top_gainers = si.get_day_gainers()
for x in range(10):
print(
str(x + 1) + '.\n' + str(top_gainers['Symbol'][x]) + '\nName: ' +
str(top_gainers['Name'][x]) + '\nPrice (Intraday): $' +
str(top_gainers['Price (Intraday)'][x]) + '\nChange ($): +$' +
str(top_gainers['Change'][x]) + '\nPercent Change: +' +
str(top_gainers["% Change"][x]) + "%\n")
def best(update, context):
stock_list = si.get_day_gainers()
output = "Stock Code \t\t\t TTM\n\n"
for i in range(0, 10):
if math.isnan(stock_list.iloc[i, -1]):
ttm_val = "---"
else:
ttm_val = str(format(stock_list.iloc[i, -1], '.2f'))
output += str(i + 1) + ". " + str(
stock_list.iloc[i, 0]).upper() + " \t\t\t " + ttm_val + ' \n'
print(output)
context.bot.send_message(chat_id=update.effective_chat.id, text=output)
def topg(self):
topgain = si.get_day_gainers()
self.listBox.delete(*self.listBox.get_children())
for x in range(25):
self.listBox.insert(
"",
"end",
values=(topgain.loc[x, 'Symbol'], topgain.loc[x, 'Name'],
'$' + str(topgain.loc[x, 'Price (Intraday)']),
'+' + str(topgain.loc[x, 'Change']),
topgain.loc[x, '% Change']))
def get_stock_movers(number_of):
gainers = get_day_gainers()
gainers = gainers.head(number_of)
gainers = gainers[["Name", "Symbol", "% Change"]]
gainers["type"] = "gainer"
losers = get_day_losers()
losers = losers.head(number_of)
losers = losers[["Name", "Symbol", "% Change"]]
losers["type"] = "loser"
losers = losers.reindex(index=losers.index[::-1])
movers = gainers.append(losers, ignore_index=True)
movers = movers.rename(columns={"% Change": "Change"})
return movers
def functions_type():
#VERIF TAILLE DU TABLEAU
if var_text.get() > 0 and var_text.get() < 101:
if type_ == "stock_gainers":
tab = si.get_day_gainers()
elif type_ == "stock_losers":
tab = si.get_day_losers()
elif type_ == "crypto":
tab = si.get_top_crypto()
else:
print("error1")
#convert in table
tab = np.array(tab)
affichage_tab(tab)
def __init__(self):
self.alpaca = tradeapi.REST(API_KEY, API_SECRET, APCA_API_BASE_URL,
'v2')
gainers = si.get_day_gainers()
gainers.columns = [
'SYM', 'Company', 'Current Price', 'Price Change',
'Percentage Change', 'Volume', 'Avg Vol', 'Market Cap',
'52 Week Range'
]
gainers.sort_values("Price Change",
axis=0,
ascending=False,
inplace=True)
end = datetime.date.today()
start = end - datetime.timedelta(days=200)
stock_watchlist = []
for stock in gainers['SYM']:
s_hist_data = si.get_data(stock,
start_date=str(start),
end_date=str(end))
var = s_hist_data.mean(axis=0, skipna=True)
avg = (float(var[0]) + float(var[1])) / 2
curr_price = si.get_live_price(stock)
if (avg < curr_price):
stock_watchlist.append(stock)
stockUniverse = stock_watchlist
print("Stock Universe for the day:", stockUniverse)
# Format the allStocks variable for use in the class.
self.allStocks = []
for stock in stockUniverse:
self.allStocks.append([stock, 0])
self.long = []
self.short = []
self.qShort = None
self.qLong = None
self.adjustedQLong = None
self.adjustedQShort = None
self.blacklist = set()
self.longAmount = 0
self.shortAmount = 0
self.timeToClose = None
def main():
print("hey")
names = []
prices = []
changes = []
percentChanges = []
marketCaps = []
totalVolumes = []
circulatingSupplys = []
result = requests.get(url)
soup = Bsoup(result.content, 'html.parser')
print("Haefd")
for listing in soup.find_all(
'tr',
attrs=
{
'class':
'simpTblRow Bgc($extraLightBlue):h BdB Bdbc($finLightGrayAlt) Bdbc($tableBorderBlue):h H(32px) Bgc(white)'
}):
for name in listing.find_all('td', attrs={'aria-label': 'Name'}):
names.append(name.text)
for price in listing.find_all('td',
attrs={'aria-label':
'Price (intraday)'}):
prices.append(price.find('span').text)
for change in listing.find_all('td', attrs={'aria-label': 'Change'}):
changes.append(change.text)
for percentChange in listing.find_all('td',
attrs={'aria-label':
'% change'}):
percentChanges.append(percentChange.text)
for marketCap in listing.find_all('td',
attrs={'aria-label': 'Market cap'}):
marketCaps.append(marketCap.text)
for totalVolume in listing.find_all(
'td', attrs={'aria-label': 'Avg vol (3-month)'}):
totalVolumes.append(totalVolume.text)
for circulatingSupply in listing.find_all(
'td', attrs={'aria-label': 'Volume'}):
circulatingSupplys.append(circulatingSupply.text)
print(len(names))
#for i in range(0, len(names)):
# print(float(changes[i][0:len(percentChanges[i]) - 1]))
# print(float(percentChanges[i][0:len(percentChanges[i])-1]))
print(si.get_day_gainers().columns) #is a pandas data frame
def Download_pulse(self):
self.pulse_result = {}
# get most active stocks on the day
if asyncio.get_event_loop().is_running(
): # Only patch if needed (i.e. running in Notebook, Spyder, etc)
import nest_asyncio
nest_asyncio.apply()
self.pulse_result['most_active'] = si.get_day_most_active()
# get biggest gainers
self.pulse_result['gainer'] = si.get_day_gainers()
# get worst performers
self.pulse_result['loser'] = si.get_day_losers()
print('_____________Market Pulse Download Done________________')
def days_gainer(self):
gainer = get_day_gainers()
df = pd.DataFrame(
gainer,
columns=['Symbol', 'Price (Intraday)', 'Change', '% Change'])
df1 = df.head(5)
print(df1)
data = []
for ind in df1.index:
dic = {
'symbol': df1['Symbol'][ind],
'price': df1['Price (Intraday)'][ind],
'change': df1['Change'][ind],
'per_change': df1['% Change'][ind]
}
data.append(dic)
return data
def morning_checkout():
gainers = si.get_day_gainers()
num_stocks_bought = 0
for stock in gainers[0:20]['Symbol']:
if (num_stocks_bought == 10):
break
stock_price = si.get_live_price(stock)
moving_avg = si.get_data(
stock,
interval='1d')['close'][-2:].mean() # check 2 day moving avg
print(stock, stock_price)
qty_d = int(100 / stock_price)
if qty_d > 0 and moving_avg > 0:
try:
buy(stock, qty_d)
num_stocks_bought += 1
except:
print("Couldn't Buy: ", stock)
continue
def get_bigger_gainers():
return si.get_day_gainers()
import requests
from yahoo_fin.stock_info import get_day_gainers
# Top Gainers of the day
data = get_day_gainers()
# Get the 5 first and transform the pandas dataframe into a list
topFive = data.iloc[0:5, 0].values.tolist()
print(topFive)
for i in range(len(topFive)):
# On récupère les données sur le site
API_URL = "https://www.alphavantage.co/query"
data = {
"function": "TIME_SERIES_INTRADAY",
"symbol": topFive[i],
"interval": "1min",
"outputsize": "compact",
"datatype": "json",
"apikey": "20M98U0442RX494K"
}
response = requests.get(API_URL, data)
data = response.json()
# Globalement les actions suivantes réduisent de plus en plus le dictionnaire pour ne donner qu'une liste de liste
# Convertit le dictionnaire en list
data = list(data.values())
# On convertit la dict globale des dates en list en gardant les dates (les keys) (intérieur est toujours dict comme ca on peut supprimer plus facilement)
data = list(data[1].items())
print(topFive[i])
print(data[0][1]['1. open'])
print(data[1][1]['1. open'])
print(0 + i, 0 + i, 0 + i, 0 + i, 0 + i, 0 + i, 0 + i, 0 + i, 0 + i, 0 + i,
import yahoo_fin.stock_info as si import ftplib, io, json, pandas, requests, requests_html #Top 100 winners today winners = si.get_day_gainers() #Top 100 losers today losers = si.get_day_losers()
import yfinance as yf
from yahoo_fin import stock_info as si
import re
# def getData(ticker):
# while True:
# data = yf.Ticker(ticker)
# print("Current Price: " + str(data.info['regularMarketPrice']))
# print("Market Open: " + str(data.info['regularMarketOpen']))
# print("Market Close: " + str(data.info['previousClose']))
# print("50 Day Average: " + str(data.info['fiftyDayAverage']))
# print("200 Day Average: " + str(data.info['twoHundredDayAverage']))
# print(data.info)
#getData('GME')
print("Enter stock ticker: ")
ticker = input()
print("Current Price: " + str(si.get_live_price(ticker)))
# get most active stocks on the day
print("Most Active:\n " + str(si.get_day_most_active()))
# get biggest gainers
print("Biggest Gainers:\n " + str(si.get_day_gainers()))
# get worst performers
print("Worst performers:\n " + str(si.get_day_losers()))
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email import encoders
import yahoo_fin.stock_info as si
import time
import datetime
#from config import *
import os
df = si.get_day_gainers()
df_filtered = df[df['% Change'] >= 5]
today = datetime.date.today()
#file_name = '/Users/shashank/Documents/Code/Python/Outputs/movers/' + "Top Gainers " + str(today) +".csv"
file_name = '/home/mmyers/Documents/GitHub/Finance/Find_Stocks/data/send_top_movers/' + "Top Gainers " + str(
today) + ".csv"
print(df)
df_filtered.to_csv(file_name)
"""
def send_email():
email_sender = junk_email_username()
email_recipient = good_email_username()
msg = MIMEMultipart()
email_message = "Attached are today's market movers"
attachment_location= file_name
msg['From'] = email_sender
msg['To'] = email_recipient
async def day_gainers(ctx):
await ctx.send(si.get_day_gainers())
from yahoo_fin import stock_info as si
from datetime import date, datetime
import pandas as pd
# gainer
si_day_gainer = si.get_day_gainers()
si_day_gainer['day_gainer'] = 'day_gainer'
si_day_gainer['modified_date'] = datetime.today().strftime('%Y-%m-%d')
si_day_gainer.to_csv('data/day_gainer/day_gainer_' +
datetime.today().strftime('%Y-%m-%d') + '.csv')
# loser
si_day_loser = si.get_day_losers()
si_day_loser['day_loser'] = 'day_loser'
si_day_loser['modified_date'] = datetime.today().strftime('%Y-%m-%d')
si_day_loser.to_csv('data/day_loser/day_loser_' +
datetime.today().strftime('%Y-%m-%d') + '.csv')
async def on_message(message):
print(message.content)
if message.content.find("!") != -1:
content = message.content.strip('!').lower().split()
print(content)
print(content[0])
if content[0] == "day":
if content[1] == "gain":
await message.channel.send(
si.get_day_gainers().head(10).iloc[:, :3])
await message.channel.send(
"-------------------------------------------")
await message.channel.send(
si.get_day_gainers().head(10).iloc[:, 4:7])
elif content[1] == "lose":
await message.channel.send(
si.get_day_losers().head(10).iloc[:, :3])
await message.channel.send(
"-------------------------------------------")
await message.channel.send(
si.get_day_losers().head(10).iloc[:, 4:7])
elif content[1] == "active":
await message.channel.send(
si.get_day_most_active().head(10).iloc[:, :3])
await message.channel.send(
"-------------------------------------------")
await message.channel.send(
si.get_day_most_active().head(10).iloc[:, 4:7])
elif content[0] == "crypto":
await message.channel.send(si.get_top_crypto().head(10).iloc[:, :3]
)
await message.channel.send(
"-------------------------------------------")
await message.channel.send(si.get_top_crypto().head(10).iloc[:,
4:5])
elif content[0] == "help":
embedVar = discord.Embed(title="List of functioning commands",
description="",
colour=0x00ff00)
embedVar.add_field(name="\u200b",
value="!tsla\n!day gain\n!day loss",
inline=True)
embedVar.add_field(
name="\u200b",
value="!calls tlsa 03/19/2021\n!puts tlsa 03/19/2021",
inline=True)
await message.channel.send(embed=embedVar)
elif content[0] == "calls":
await message.channel.send(
op.get_calls(content[1], content[2]).iloc[:, 2:8])
elif content[0] == "puts":
await message.channel.send(
op.get_puts(content[1], content[2]).iloc[:, 2:8])
else:
temp = si.get_quote_table(content[0])
change = round(temp["Quote Price"] - temp["Previous Close"], 2)
percentage = round(change / temp["Previous Close"] * 100, 2)
displayQuote = str(round(temp["Quote Price"], 2))
displayChange = str(change)
displayPercentage = str(percentage)
displayTicker = content[0].upper()
displayClose = str(round(temp["Previous Close"], 2))
dayRange = temp["Day's Range"].replace('-', '').split()
dayLow = dayRange[0]
dayHigh = dayRange[1]
open = temp["Open"]
close = temp["Previous Close"]
volume = str(round(temp["Volume"] / 1000000, 2))
volume = volume + "M"
avgVolume = str(round(temp["Avg. Volume"] / 1000000, 2))
avgVolume = avgVolume + "M"
bid = temp["Bid"]
ask = temp["Ask"]
if change >= 0:
rgb = 0x00ff00
displayChange = "+" + displayChange
displayPercentage = "+" + displayPercentage
else:
rgb = 0xff0000
embedVar = discord.Embed(
title=
f"${displayTicker}\n${displayQuote} {displayChange} ({displayPercentage}%)",
description="",
colour=rgb)
embedVar.add_field(
name="\u200b",
value=
f"High: {dayHigh}\nLow: {dayLow}\n\nAsk: {ask}\nBid: {bid}",
inline=True)
embedVar.add_field(name="\u200b",
value=f"Open: {open}\nPrev.: {close}",
inline=True)
embedVar.add_field(
name="\u200b",
value=f"Volume: {volume}\nAvg. Vol.: {avgVolume}",
inline=True)
await message.channel.send(embed=embedVar)
import matplotlib.pyplot as plt
from datetime import date
import yfinance as yf
import pandas as pd
from yahoo_fin import stock_info as si
import mplcursors
symbol1 = "AAPL"
stockOne = yf.Ticker(symbol1)
dailyData = pd.DataFrame(stockOne.history(period="1d", interval="1m"))['Open']
dailyData.index = dailyData.index.strftime("%H:%M:%S")
gainers = pd.DataFrame(si.get_day_gainers())[['Price (Intraday)', '% Change']]
gainers.index = pd.DataFrame(si.get_day_gainers())['Symbol']
losers = pd.DataFrame(si.get_day_losers())[['Price (Intraday)', '% Change']]
losers.index = pd.DataFrame(si.get_day_losers())['Symbol']
active = pd.DataFrame(
si.get_day_most_active())[['Price (Intraday)', '% Change']]
active.index = pd.DataFrame(si.get_day_most_active())['Symbol']
print(pd.DataFrame(si.tickers_dow()))
